AI Bookkeeper Evaluation Checklist
An AI bookkeeper evaluation checklist covers what to ask before you commit: what exactly does it do (and not do), how does it handle unclear invoices, how is accuracy measured, who sees your financial data, and what does it cost per invoice — including the invoices it gets wrong. The honest test is not whether the tool sounds impressive in a demo; it is whether it handles your messiest real invoice correctly and tells you when it cannot.
Evaluating an AI bookkeeper is hard because the marketing sounds the same across every product — “automated”, “accurate”, “seamless”. The differences that matter are not in the demo, where everything works. They are in the edge cases: the invoice with a blurry scan, the duplicate number from a supplier who reissued, the VAT rate that changed mid-year. This checklist is the set of questions that separates a tool that actually does the work from one that looks like it does. It includes the questions vendors prefer not to answer.
What it does — and does not
Ask exactly which fields it extracts, on which invoice formats
“Processes invoices” is not a specification. A tool that extracts supplier, date, invoice number, net, VAT and total from a clean PDF is very different from one that only reads the total from a photo. The format it handles best tells you what kind of invoices it will actually work on for you.
Ask what it does when it is not sure
The most important behaviour of an AI bookkeeper is not how it handles a clean invoice — it is what happens when something is unclear. A tool that guesses silently is dangerous; one that flags and waits for you is trustworthy. This single behaviour is the difference between books you can rely on and books you have to recheck.
Ask whether it enters invoices or also approves payments
These are different jobs with different risks. Entering an invoice is reversible; approving a payment is not. A tool that does both, automatically, removes the last human checkpoint before money leaves your bank. Knowing where the automation stops tells you where your oversight needs to start.
Accuracy & cost
Ask how accuracy is measured — and on whose invoices
A vendor’s accuracy claim is only meaningful if you know the test set. “99% accurate” on clean, templated invoices from a dozen large suppliers tells you nothing about how the tool performs on the messy, handwritten or multi-format invoices your business actually receives. Ask for the breakdown, not the headline.
Ask what you pay for invoices it gets wrong
Per-invoice pricing only matters if you know what counts as “processed”. If you pay for every invoice the tool touches — including the ones it enters incorrectly and you have to redo — the real cost per correct invoice is far higher than the headline price. Pay only for invoices that are actually completed and correct.
Data & trust
Ask who can see your financial data, and where it is stored
Supplier invoices reveal who you buy from, what you pay, and your cost structure. That is sensitive commercial data. A tool that sends your invoices to a third-party AI provider for processing — or trains on your data — is making a trade-off you should understand before you commit, not after.
Test it on your messiest real invoice before committing
A demo uses the vendor’s best invoices. Your business has its own: the supplier who handwrites delivery notes, the scan that came through sideways, the invoice in a second language. A two-week test on your actual invoices tells you more than any sales call — and it is the only way to know if the tool fits your reality.
How Nika helps
Nika is built to be evaluated against this checklist. She extracts all six standard fields, flags anything she is not sure about instead of guessing, never approves payments, and you pay only for invoices she completes — not for the ones she sends back to you. We publish what she does and does not do, because the evaluation is the point: a tool you cannot evaluate is a tool you cannot trust.
Questions
What is the most important question to ask an AI bookkeeper vendor?
“What does it do when it is not sure?” A tool that guesses silently is a liability — it puts wrong entries into your books that you discover later. A tool that flags uncertainty and waits for you is one you can actually trust with your books. Everything else — accuracy percentages, feature lists — is secondary to this one behaviour.
How do I test Nika against this checklist?
Forward a sample of your real supplier invoices — including the messy ones — to her mailbox for two weeks. You will see which fields she extracts, how she handles unclear invoices, and what you are charged. There is no setup fee and no minimum; the test costs only the invoices she completes, at {price} each.